Special Issue "Algorithmic Game Theory"

Guest Editor
Dr. George Karakostas
Department of Computing and Software, Faculty of Engineering, McMaster University, 1280 Main St. West, Hamilton, Ontario L8S 4K1, Canada
Website: http://www.cas.mcmaster.ca/~gk/
E-Mail:
Interests: design and mathematical analysis of algorithms; combinatorial optimization; approximation algorithms; algorithmic game theory; network algorithms

Related MDPI Journal: Games

Submission

All papers should be submitted to algorithms@mdpi.org. To be published continuously until the deadline and papers will be listed together at the special issue website.

Submitted papers should not have been published nor be under consideration for publication elsewhere. All papers are refereed through a peer-review process. A guide for authors is available on the Instructions for Authors page. Algorithms is an international peer-reviewed quarterly journal published by Molecular Diversity Preservation International.

Article Processing Charges (APC) will be waived for well prepared manuscripts of invited papers. For the first three volumes of this new journal the APC are of 300 CHF (or 550 CHF per paper for those papers that require extensive additional formatting and/or English corrections) for papers submitted before 31 December 2010.

Title: Game Theoretical Evolutionary Simulation: a Method for Crafting and Analyzing Competition Models
Author: Y. Caseau
Affiliation: Bouygues Telecom, 20 Quai du Point du Jour, Boulogne-Billancourt Telecom, France; E-Mail: yves@caseau.com
Abstract: This paper proposes a framework, based upon game theory and optimization through machine learning, for designing actors’ system models. This framework (GTES) is well suited to study the behavior of companies that compete in an open market. The method relies on combining three techniques: sampling as a simulation tool (Monte-Carlo), game theory as far as the search of equilibriums between competing strategies is concerned and local optimization meta-heuristics, such as genetic algorithms. This combination is not original as such, although it is rarely used to the full extent of the three techniques joined together. Our contribution in this paper is twofold. On the one hand, we propose a framework which supports in a systematic and replicable manner the cooperation between there three paradigms. On the other hand, we propose different approaches to find Nash equilibriums between the players’ strategies, in a strict or an extended manner. The concept of “extension” is based on a weaker equilibrium definition, inspired either from genetic algorithms or minmax heuristics. This GTES framework is a simulation method, which is not designed to solve enterprise/competition problems but rather to study mathematical models associated to these problems. As such, it is a foundation for “serious games”, where participants learn about the properties of a given situation by running multiple simulations. We illustrate GTES with two examples drawn from the mobile phone industry.
Keywords: simulation; machine learning; game theory; evolutionary algorithms; enterprise modeling; “serious games”